Machine-learning modeling frameworks can broadly be divided into discriminative and generative categories. Discriminative frameworks take a simplistic approach by modeling the underlying process as a particular variable. While this is adequate for many applications, the actual processes are, in fact, much more complex. For example, in electroencephalography (EEG) based seizure detection using a discriminative model such as a support-vector-based classification, in which an application simply focuses on detecting seizures or non-seizures. In fact, neurological processes that lead to a seizure are more complicated, as there are dynamics during the onset of the seizure. Data representing this underlying dynamic gradually traverses a feature space from a non-seizure space, and eventually crosses the decision boundary to a seizure space.
Generative frameworks attempt to model the underlying processes more richly for applications where such characteristics might be important. For example, during a sleep stage, monitoring a combination of Gaussian mixture models (GMMs) and hidden Markov models (HMMs) is used to identify a sleep stage by detecting patterns of state transitions.
While physiological signals for biomedical applications have some of the most analytically intractable features, other non-biomedical such as robotics benefit from machine-learning. For example, machine-learning is employed in robotics for 3D scene analysis and manipulator actuation and so forth. As such, many of the same types of machine-learning frameworks are used in biomedical applications and employed in robotics.
These examples illustrate that different modeling frameworks are required to address the processes encountered in different applications. Thus, there is a need for a machine-learning accelerator (MLA) integrated circuit to support a range of computations required in these various machine-learning frameworks while employing a specialized architecture that can exploit the algorithmic structure described previously in order to achieve low energy.